Статья

Modeling of thermal energy saving in commercial buildings of Australia by balanced tree growth optimizer

Xiuhong BaiShandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, 262700, ChinaYasser FouadDepartment of Applied Mechanical Engineering, College of Applied Engineering, Muzahimiyah Branch, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi ArabiaC. ChiranjeeviSchool of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, IndiaSalem AlkhalafDepartment of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Qassim, Saudi ArabiaBarno Sayfutdinovna AbdullaevaVice-Rector for Scientific Affairs, Tashkent State Pedagogical University, Tashkent, UzbekistanFawaz S. AlharbiDepartment of Mechanical Engineering, College of Engineering, University of Hafr Al Batin, P.O. Box 1803, Hafr Al Batin, 39524, Saudi ArabiaLaith H. AlzubaidiCollege of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, IraqZuhair JastaneyahDepartment of Mechanical Engineering, College of Engineering, University of Business and Technology, Jeddah, 21361, Saudi ArabiaHakim AL GarallehDepartment of Mathematical Science, College of Engineering, University of Business and Technology, Jeddah, 21361, Saudi Arabia

Case Studies in Thermal Engineeringjournal2024en

ABI

Аннотация

Low-energy cases can be designed more efficiently by using analytical optimization. The non-linear thermal performance of buildings has led to the development of optimization techniques based on simulation. In building optimization, it is important to achieve superior solutions while minimizing calculation expenses. This study aims to optimize an Australian office building using the Balanced Tree Growth Optimizer (BTGO). It has resulted that more than 11.7 % of energy can be saved by the optimization process and also some energy-saving measures. A comparison of the utilized algorithm with benchmark algorithms including the Nelder-Mead method, hybrid Particle Swarm Optimization, Hooke-Jeeves, and Ant Colony Optimization for continuous domain showed that the BTGO can achieve better solutions and needs less computational time.

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Темы

Building Energy and Comfort Optimization Advanced Multi-Objective Optimization Algorithms Wind and Air Flow Studies

Идентификаторы

DOI: 10.1016/j.csite.2024.104028

Цитирования и источники

Цитирований: 1 Использованных источников: 21

Показатели — AkademScholar